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  <div class="section" id="the-python-profilers">
<span id="profile"></span><h1>Python 分析器<a class="headerlink" href="#the-python-profilers" title="永久链接至标题">¶</a></h1>
<p><strong>源代码：</strong> <a class="reference external" href="https://github.com/python/cpython/tree/3.7/Lib/profile.py">Lib/profile.py</a> and <a class="reference external" href="https://github.com/python/cpython/tree/3.7/Lib/pstats.py">Lib/pstats.py</a></p>
<hr class="docutils" />
<div class="section" id="introduction-to-the-profilers">
<span id="profiler-introduction"></span><h2>分析器简介<a class="headerlink" href="#introduction-to-the-profilers" title="永久链接至标题">¶</a></h2>
<p id="index-0"><a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> 和 <a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 提供了 Python 程序的 <em class="dfn">deterministic profiling</em> 。 <em class="dfn">profile</em> 是一组统计数据，描述程序的各个部分执行的频率和时间。这些统计数据可以通过 <a class="reference internal" href="#module-pstats" title="pstats: Statistics object for use with the profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">pstats</span></code></a> 模块格式化为报告。</p>
<p>Python 标准库提供了同一分析接口的两种不同实现：</p>
<ol class="arabic simple">
<li><p>对于大多数用户，建议使用 <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> ；这是一个C扩展插件，开销合理，适合于分析长时间运行的程序。该插件基于 <code class="xref py py-mod docutils literal notranslate"><span class="pre">lsprof</span></code> ，由 Brett Rosen 和 Ted Chaotter 贡献。</p></li>
<li><p><a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 是一个纯 Python 模块（<a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> 就是模仿其接口的 C 实现），但它会显著增加配置程序的开销。如果你正在尝试以某种方式扩展分析器，则使用此模块可能会更容易完成任务。该模块最初由Jim Roskind 设计和编写。</p></li>
</ol>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>profiler 分析器模块被设计为给指定的程序提供执行概要文件，而不是用于基准测试目的（ <a class="reference internal" href="timeit.html#module-timeit" title="timeit: Measure the execution time of small code snippets."><code class="xref py py-mod docutils literal notranslate"><span class="pre">timeit</span></code></a> 才是用于此目标的，它能获得合理准确的结果）。这特别适用于将 Python 代码与 C 代码进行基准测试：分析器为Python 代码引入开销，但不会为C级函数引入开销，因此 C 代码似乎比任何Python 代码都更快。</p>
</div>
</div>
<div class="section" id="instant-user-s-manual">
<span id="profile-instant"></span><h2>即时用户手册<a class="headerlink" href="#instant-user-s-manual" title="永久链接至标题">¶</a></h2>
<p>本节是为 “不想阅读手册” 的用户提供的。它提供了非常简短的概述，并允许用户快速对现有应用程序执行评测。</p>
<p>要分析采用单个参数的函数，可以执行以下操作：</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cProfile</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="n">cProfile</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="s1">&#39;re.compile(&quot;foo|bar&quot;)&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>（如果 <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> 在您的系统上不可用，请使用 <a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 。）</p>
<p>上述操作将运行 <a class="reference internal" href="re.html#re.compile" title="re.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">re.compile()</span></code></a> 并打印分析结果，如下所示：</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span>      <span class="mi">197</span> <span class="n">function</span> <span class="n">calls</span> <span class="p">(</span><span class="mi">192</span> <span class="n">primitive</span> <span class="n">calls</span><span class="p">)</span> <span class="ow">in</span> <span class="mf">0.002</span> <span class="n">seconds</span>

<span class="n">Ordered</span> <span class="n">by</span><span class="p">:</span> <span class="n">standard</span> <span class="n">name</span>

<span class="n">ncalls</span>  <span class="n">tottime</span>  <span class="n">percall</span>  <span class="n">cumtime</span>  <span class="n">percall</span> <span class="n">filename</span><span class="p">:</span><span class="n">lineno</span><span class="p">(</span><span class="n">function</span><span class="p">)</span>
     <span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.001</span>    <span class="mf">0.001</span> <span class="o">&lt;</span><span class="n">string</span><span class="o">&gt;</span><span class="p">:</span><span class="mi">1</span><span class="p">(</span><span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span><span class="p">)</span>
     <span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.001</span>    <span class="mf">0.001</span> <span class="n">re</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">212</span><span class="p">(</span><span class="nb">compile</span><span class="p">)</span>
     <span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.001</span>    <span class="mf">0.001</span> <span class="n">re</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">268</span><span class="p">(</span><span class="n">_compile</span><span class="p">)</span>
     <span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span> <span class="n">sre_compile</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">172</span><span class="p">(</span><span class="n">_compile_charset</span><span class="p">)</span>
     <span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span> <span class="n">sre_compile</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">201</span><span class="p">(</span><span class="n">_optimize_charset</span><span class="p">)</span>
     <span class="mi">4</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span> <span class="n">sre_compile</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">25</span><span class="p">(</span><span class="n">_identityfunction</span><span class="p">)</span>
   <span class="mi">3</span><span class="o">/</span><span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span>    <span class="mf">0.000</span> <span class="n">sre_compile</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">33</span><span class="p">(</span><span class="n">_compile</span><span class="p">)</span>
</pre></div>
</div>
<p>第一行显示监听了197个调用。在这些调用中，有192个是 <em class="dfn">原始的</em> ，这意味着调用不是通过递归引发的。下一行: <code class="docutils literal notranslate"><span class="pre">Ordered</span> <span class="pre">by:</span> <span class="pre">standard</span> <span class="pre">name</span></code> ，表示最右边列中的文本字符串用于对输出进行排序。列标题包括：</p>
<dl class="simple">
<dt>ncalls</dt><dd><p>调用次数</p>
</dd>
<dt>tottime</dt><dd><p>在指定函数中花费的总时间（不包括调用子函数的时间）</p>
</dd>
<dt>percall</dt><dd><p>是 <code class="docutils literal notranslate"><span class="pre">tottime</span></code> 除以 <code class="docutils literal notranslate"><span class="pre">ncalls</span></code> 的商</p>
</dd>
<dt>cumtime</dt><dd><p>指定的函数及其所有子函数（从调用到退出）消耗的累积时间。这个数字对于递归函数来说是准确的。</p>
</dd>
<dt>percall</dt><dd><p>是 <code class="docutils literal notranslate"><span class="pre">cumtime</span></code> 除以原始调用（次数）的商</p>
</dd>
<dt>filename:lineno(function)</dt><dd><p>提供相应数据的每个函数</p>
</dd>
</dl>
<p>如果第一列中有两个数字（例如3/1），则表示函数递归。第二个值是原始调用次数，第一个是调用的总次数。请注意，当函数不递归时，这两个值是相同的，并且只打印单个数字。</p>
<p>profile 运行结束时，打印输出不是必须的。也可以通过为 <code class="xref py py-func docutils literal notranslate"><span class="pre">run()</span></code> 函数指定文件名，将结果保存到文件中：</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cProfile</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="n">cProfile</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="s1">&#39;re.compile(&quot;foo|bar&quot;)&#39;</span><span class="p">,</span> <span class="s1">&#39;restats&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p><a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">pstats.Stats</span></code></a> 类从文件中读取 profile 结果，并以各种方式对其进行格式化。</p>
<p>The file <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> can also be invoked as a script to profile another
script.  For example:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">cProfile</span> <span class="p">[</span><span class="o">-</span><span class="n">o</span> <span class="n">output_file</span><span class="p">]</span> <span class="p">[</span><span class="o">-</span><span class="n">s</span> <span class="n">sort_order</span><span class="p">]</span> <span class="p">(</span><span class="o">-</span><span class="n">m</span> <span class="n">module</span> <span class="o">|</span> <span class="n">myscript</span><span class="o">.</span><span class="n">py</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">-o</span></code> 将profile 结果写入文件而不是标准输出</p>
<p><code class="docutils literal notranslate"><span class="pre">-s</span></code> 指定 <a class="reference internal" href="#pstats.Stats.sort_stats" title="pstats.Stats.sort_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">sort_stats()</span></code></a> 排序值之一以对输出进行排序。这仅适用于未提供 <code class="docutils literal notranslate"><span class="pre">-o</span></code> 的情况</p>
<p><code class="docutils literal notranslate"><span class="pre">-m</span></code> 指定要分析的是模块而不是脚本。</p>
<blockquote>
<div><div class="versionadded">
<p><span class="versionmodified added">3.7 新版功能: </span>Added the <code class="docutils literal notranslate"><span class="pre">-m</span></code> option.</p>
</div>
</div></blockquote>
<p>The <a class="reference internal" href="#module-pstats" title="pstats: Statistics object for use with the profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">pstats</span></code></a> module's <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class has a variety of methods
for manipulating and printing the data saved into a profile results file:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pstats</span>
<span class="kn">from</span> <span class="nn">pstats</span> <span class="kn">import</span> <span class="n">SortKey</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">pstats</span><span class="o">.</span><span class="n">Stats</span><span class="p">(</span><span class="s1">&#39;restats&#39;</span><span class="p">)</span>
<span class="n">p</span><span class="o">.</span><span class="n">strip_dirs</span><span class="p">()</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">print_stats</span><span class="p">()</span>
</pre></div>
</div>
<p>The <a class="reference internal" href="#pstats.Stats.strip_dirs" title="pstats.Stats.strip_dirs"><code class="xref py py-meth docutils literal notranslate"><span class="pre">strip_dirs()</span></code></a> method removed the extraneous path from all
the module names. The <a class="reference internal" href="#pstats.Stats.sort_stats" title="pstats.Stats.sort_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">sort_stats()</span></code></a> method sorted all the
entries according to the standard module/line/name string that is printed. The
<a class="reference internal" href="#pstats.Stats.print_stats" title="pstats.Stats.print_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">print_stats()</span></code></a> method printed out all the statistics.  You
might try the following sort calls:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="n">SortKey</span><span class="o">.</span><span class="n">NAME</span><span class="p">)</span>
<span class="n">p</span><span class="o">.</span><span class="n">print_stats</span><span class="p">()</span>
</pre></div>
</div>
<p>The first call will actually sort the list by function name, and the second call
will print out the statistics.  The following are some interesting calls to
experiment with:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="n">SortKey</span><span class="o">.</span><span class="n">CUMULATIVE</span><span class="p">)</span><span class="o">.</span><span class="n">print_stats</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>This sorts the profile by cumulative time in a function, and then only prints
the ten most significant lines.  If you want to understand what algorithms are
taking time, the above line is what you would use.</p>
<p>If you were looking to see what functions were looping a lot, and taking a lot
of time, you would do:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="n">SortKey</span><span class="o">.</span><span class="n">TIME</span><span class="p">)</span><span class="o">.</span><span class="n">print_stats</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>to sort according to time spent within each function, and then print the
statistics for the top ten functions.</p>
<p>您也可以尝试：</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="n">SortKey</span><span class="o">.</span><span class="n">FILENAME</span><span class="p">)</span><span class="o">.</span><span class="n">print_stats</span><span class="p">(</span><span class="s1">&#39;__init__&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>This will sort all the statistics by file name, and then print out statistics
for only the class init methods (since they are spelled with <code class="docutils literal notranslate"><span class="pre">__init__</span></code> in
them).  As one final example, you could try:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="n">SortKey</span><span class="o">.</span><span class="n">TIME</span><span class="p">,</span> <span class="n">SortKey</span><span class="o">.</span><span class="n">CUMULATIVE</span><span class="p">)</span><span class="o">.</span><span class="n">print_stats</span><span class="p">(</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;init&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>This line sorts statistics with a primary key of time, and a secondary key of
cumulative time, and then prints out some of the statistics. To be specific, the
list is first culled down to 50% (re: <code class="docutils literal notranslate"><span class="pre">.5</span></code>) of its original size, then only
lines containing <code class="docutils literal notranslate"><span class="pre">init</span></code> are maintained, and that sub-sub-list is printed.</p>
<p>If you wondered what functions called the above functions, you could now (<code class="docutils literal notranslate"><span class="pre">p</span></code>
is still sorted according to the last criteria) do:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">print_callers</span><span class="p">(</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;init&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>and you would get a list of callers for each of the listed functions.</p>
<p>If you want more functionality, you're going to have to read the manual, or
guess what the following functions do:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">.</span><span class="n">print_callees</span><span class="p">()</span>
<span class="n">p</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;restats&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Invoked as a script, the <a class="reference internal" href="#module-pstats" title="pstats: Statistics object for use with the profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">pstats</span></code></a> module is a statistics browser for
reading and examining profile dumps.  It has a simple line-oriented interface
(implemented using <a class="reference internal" href="cmd.html#module-cmd" title="cmd: Build line-oriented command interpreters."><code class="xref py py-mod docutils literal notranslate"><span class="pre">cmd</span></code></a>) and interactive help.</p>
</div>
<div class="section" id="module-cProfile">
<span id="profile-and-cprofile-module-reference"></span><h2><a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 和 <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> 模块参考<a class="headerlink" href="#module-cProfile" title="永久链接至标题">¶</a></h2>
<span class="target" id="module-profile"></span><p><a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 和 <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> 模块都提供下列函数：</p>
<dl class="function">
<dt id="profile.run">
<code class="sig-prename descclassname">profile.</code><code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param">command</em>, <em class="sig-param">filename=None</em>, <em class="sig-param">sort=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.run" title="永久链接至目标">¶</a></dt>
<dd><p>This function takes a single argument that can be passed to the <a class="reference internal" href="functions.html#exec" title="exec"><code class="xref py py-func docutils literal notranslate"><span class="pre">exec()</span></code></a>
function, and an optional file name.  In all cases this routine executes:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">exec</span><span class="p">(</span><span class="n">command</span><span class="p">,</span> <span class="n">__main__</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">,</span> <span class="n">__main__</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
</pre></div>
</div>
<p>and gathers profiling statistics from the execution. If no file name is
present, then this function automatically creates a <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a>
instance and prints a simple profiling report. If the sort value is specified,
it is passed to this <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> instance to control how the
results are sorted.</p>
</dd></dl>

<dl class="function">
<dt id="profile.runctx">
<code class="sig-prename descclassname">profile.</code><code class="sig-name descname">runctx</code><span class="sig-paren">(</span><em class="sig-param">command</em>, <em class="sig-param">globals</em>, <em class="sig-param">locals</em>, <em class="sig-param">filename=None</em>, <em class="sig-param">sort=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.runctx" title="永久链接至目标">¶</a></dt>
<dd><p>This function is similar to <a class="reference internal" href="#profile.run" title="profile.run"><code class="xref py py-func docutils literal notranslate"><span class="pre">run()</span></code></a>, with added arguments to supply the
globals and locals dictionaries for the <em>command</em> string. This routine
executes:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">exec</span><span class="p">(</span><span class="n">command</span><span class="p">,</span> <span class="nb">globals</span><span class="p">,</span> <span class="nb">locals</span><span class="p">)</span>
</pre></div>
</div>
<p>and gathers profiling statistics as in the <a class="reference internal" href="#profile.run" title="profile.run"><code class="xref py py-func docutils literal notranslate"><span class="pre">run()</span></code></a> function above.</p>
</dd></dl>

<dl class="class">
<dt id="profile.Profile">
<em class="property">class </em><code class="sig-prename descclassname">profile.</code><code class="sig-name descname">Profile</code><span class="sig-paren">(</span><em class="sig-param">timer=None</em>, <em class="sig-param">timeunit=0.0</em>, <em class="sig-param">subcalls=True</em>, <em class="sig-param">builtins=True</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile" title="永久链接至目标">¶</a></dt>
<dd><p>This class is normally only used if more precise control over profiling is
needed than what the <code class="xref py py-func docutils literal notranslate"><span class="pre">cProfile.run()</span></code> function provides.</p>
<p>A custom timer can be supplied for measuring how long code takes to run via
the <em>timer</em> argument. This must be a function that returns a single number
representing the current time. If the number is an integer, the <em>timeunit</em>
specifies a multiplier that specifies the duration of each unit of time. For
example, if the timer returns times measured in thousands of seconds, the
time unit would be <code class="docutils literal notranslate"><span class="pre">.001</span></code>.</p>
<p>Directly using the <a class="reference internal" href="#profile.Profile" title="profile.Profile"><code class="xref py py-class docutils literal notranslate"><span class="pre">Profile</span></code></a> class allows formatting profile results
without writing the profile data to a file:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cProfile</span><span class="o">,</span> <span class="nn">pstats</span><span class="o">,</span> <span class="nn">io</span>
<span class="kn">from</span> <span class="nn">pstats</span> <span class="kn">import</span> <span class="n">SortKey</span>
<span class="n">pr</span> <span class="o">=</span> <span class="n">cProfile</span><span class="o">.</span><span class="n">Profile</span><span class="p">()</span>
<span class="n">pr</span><span class="o">.</span><span class="n">enable</span><span class="p">()</span>
<span class="c1"># ... do something ...</span>
<span class="n">pr</span><span class="o">.</span><span class="n">disable</span><span class="p">()</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">()</span>
<span class="n">sortby</span> <span class="o">=</span> <span class="n">SortKey</span><span class="o">.</span><span class="n">CUMULATIVE</span>
<span class="n">ps</span> <span class="o">=</span> <span class="n">pstats</span><span class="o">.</span><span class="n">Stats</span><span class="p">(</span><span class="n">pr</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">sort_stats</span><span class="p">(</span><span class="n">sortby</span><span class="p">)</span>
<span class="n">ps</span><span class="o">.</span><span class="n">print_stats</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">getvalue</span><span class="p">())</span>
</pre></div>
</div>
<dl class="method">
<dt id="profile.Profile.enable">
<code class="sig-name descname">enable</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.enable" title="永久链接至目标">¶</a></dt>
<dd><p>Start collecting profiling data.</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.disable">
<code class="sig-name descname">disable</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.disable" title="永久链接至目标">¶</a></dt>
<dd><p>Stop collecting profiling data.</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.create_stats">
<code class="sig-name descname">create_stats</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.create_stats" title="永久链接至目标">¶</a></dt>
<dd><p>停止收集分析数据，并在内部将结果记录为当前 profile。</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.print_stats">
<code class="sig-name descname">print_stats</code><span class="sig-paren">(</span><em class="sig-param">sort=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.print_stats" title="永久链接至目标">¶</a></dt>
<dd><p>Create a <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> object based on the current
profile and print the results to stdout.</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.dump_stats">
<code class="sig-name descname">dump_stats</code><span class="sig-paren">(</span><em class="sig-param">filename</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.dump_stats" title="永久链接至目标">¶</a></dt>
<dd><p>将当前profile 的结果写入 <em>filename</em> 。</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.run">
<code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param">cmd</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.run" title="永久链接至目标">¶</a></dt>
<dd><p>Profile the cmd via <a class="reference internal" href="functions.html#exec" title="exec"><code class="xref py py-func docutils literal notranslate"><span class="pre">exec()</span></code></a>.</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.runctx">
<code class="sig-name descname">runctx</code><span class="sig-paren">(</span><em class="sig-param">cmd</em>, <em class="sig-param">globals</em>, <em class="sig-param">locals</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.runctx" title="永久链接至目标">¶</a></dt>
<dd><p>Profile the cmd via <a class="reference internal" href="functions.html#exec" title="exec"><code class="xref py py-func docutils literal notranslate"><span class="pre">exec()</span></code></a> with the specified global and
local environment.</p>
</dd></dl>

<dl class="method">
<dt id="profile.Profile.runcall">
<code class="sig-name descname">runcall</code><span class="sig-paren">(</span><em class="sig-param">func</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#profile.Profile.runcall" title="永久链接至目标">¶</a></dt>
<dd><p>Profile <code class="docutils literal notranslate"><span class="pre">func(*args,</span> <span class="pre">**kwargs)</span></code></p>
</dd></dl>

</dd></dl>

<p>Note that profiling will only work if the called command/function actually
returns.  If the interpreter is terminated (e.g. via a <a class="reference internal" href="sys.html#sys.exit" title="sys.exit"><code class="xref py py-func docutils literal notranslate"><span class="pre">sys.exit()</span></code></a> call
during the called command/function execution) no profiling results will be
printed.</p>
</div>
<div class="section" id="the-stats-class">
<span id="profile-stats"></span><h2><a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> 类<a class="headerlink" href="#the-stats-class" title="永久链接至标题">¶</a></h2>
<p>Analysis of the profiler data is done using the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class.</p>
<span class="target" id="module-pstats"></span><dl class="class">
<dt id="pstats.Stats">
<em class="property">class </em><code class="sig-prename descclassname">pstats.</code><code class="sig-name descname">Stats</code><span class="sig-paren">(</span><em class="sig-param">*filenames or profile</em>, <em class="sig-param">stream=sys.stdout</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats" title="永久链接至目标">¶</a></dt>
<dd><p>This class constructor creates an instance of a &quot;statistics object&quot; from a
<em>filename</em> (or list of filenames) or from a <code class="xref py py-class docutils literal notranslate"><span class="pre">Profile</span></code> instance. Output
will be printed to the stream specified by <em>stream</em>.</p>
<p>The file selected by the above constructor must have been created by the
corresponding version of <a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> or <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a>.  To be specific,
there is <em>no</em> file compatibility guaranteed with future versions of this
profiler, and there is no compatibility with files produced by other
profilers, or the same profiler run on a different operating system.  If
several files are provided, all the statistics for identical functions will
be coalesced, so that an overall view of several processes can be considered
in a single report.  If additional files need to be combined with data in an
existing <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> object, the <a class="reference internal" href="#pstats.Stats.add" title="pstats.Stats.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a> method
can be used.</p>
<p>Instead of reading the profile data from a file, a <code class="xref py py-class docutils literal notranslate"><span class="pre">cProfile.Profile</span></code>
or <a class="reference internal" href="#profile.Profile" title="profile.Profile"><code class="xref py py-class docutils literal notranslate"><span class="pre">profile.Profile</span></code></a> object can be used as the profile data source.</p>
<p><a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> 对象有以下方法:</p>
<dl class="method">
<dt id="pstats.Stats.strip_dirs">
<code class="sig-name descname">strip_dirs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.strip_dirs" title="永久链接至目标">¶</a></dt>
<dd><p>This method for the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class removes all leading path
information from file names.  It is very useful in reducing the size of
the printout to fit within (close to) 80 columns.  This method modifies
the object, and the stripped information is lost.  After performing a
strip operation, the object is considered to have its entries in a
&quot;random&quot; order, as it was just after object initialization and loading.
If <a class="reference internal" href="#pstats.Stats.strip_dirs" title="pstats.Stats.strip_dirs"><code class="xref py py-meth docutils literal notranslate"><span class="pre">strip_dirs()</span></code></a> causes two function names to be
indistinguishable (they are on the same line of the same filename, and
have the same function name), then the statistics for these two entries
are accumulated into a single entry.</p>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.add">
<code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">*filenames</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.add" title="永久链接至目标">¶</a></dt>
<dd><p>This method of the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class accumulates additional profiling
information into the current profiling object.  Its arguments should refer
to filenames created by the corresponding version of <a class="reference internal" href="#profile.run" title="profile.run"><code class="xref py py-func docutils literal notranslate"><span class="pre">profile.run()</span></code></a>
or <code class="xref py py-func docutils literal notranslate"><span class="pre">cProfile.run()</span></code>. Statistics for identically named (re: file, line,
name) functions are automatically accumulated into single function
statistics.</p>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.dump_stats">
<code class="sig-name descname">dump_stats</code><span class="sig-paren">(</span><em class="sig-param">filename</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.dump_stats" title="永久链接至目标">¶</a></dt>
<dd><p>Save the data loaded into the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> object to a file named
<em>filename</em>.  The file is created if it does not exist, and is overwritten
if it already exists.  This is equivalent to the method of the same name
on the <a class="reference internal" href="#profile.Profile" title="profile.Profile"><code class="xref py py-class docutils literal notranslate"><span class="pre">profile.Profile</span></code></a> and <code class="xref py py-class docutils literal notranslate"><span class="pre">cProfile.Profile</span></code> classes.</p>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.sort_stats">
<code class="sig-name descname">sort_stats</code><span class="sig-paren">(</span><em class="sig-param">*keys</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.sort_stats" title="永久链接至目标">¶</a></dt>
<dd><p>This method modifies the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> object by sorting it according to
the supplied criteria.  The argument can be either a string or a SortKey
enum identifying the basis of a sort (example: <code class="docutils literal notranslate"><span class="pre">'time'</span></code>, <code class="docutils literal notranslate"><span class="pre">'name'</span></code>,
<code class="docutils literal notranslate"><span class="pre">SortKey.TIME</span></code> or <code class="docutils literal notranslate"><span class="pre">SortKey.NAME</span></code>). The SortKey enums argument have
advantage over the string argument in that it is more robust and less
error prone.</p>
<p>When more than one key is provided, then additional keys are used as
secondary criteria when there is equality in all keys selected before
them.  For example, <code class="docutils literal notranslate"><span class="pre">sort_stats(SortKey.NAME,</span> <span class="pre">SortKey.FILE)</span></code> will sort
all the entries according to their function name, and resolve all ties
(identical function names) by sorting by file name.</p>
<p>For the string argument, abbreviations can be used for any key names, as
long as the abbreviation is unambiguous.</p>
<p>The following are the valid string and SortKey:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 30%" />
<col style="width: 34%" />
<col style="width: 36%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>有效字符串参数</p></th>
<th class="head"><p>有效枚举参数</p></th>
<th class="head"><p>含义</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'calls'</span></code></p></td>
<td><p>SortKey.CALLS</p></td>
<td><p>调用次数</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'cumulative'</span></code></p></td>
<td><p>SortKey.CUMULATIVE</p></td>
<td><p>累积时间</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'cumtime'</span></code></p></td>
<td><p>N/A</p></td>
<td><p>累积时间</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'file'</span></code></p></td>
<td><p>N/A</p></td>
<td><p>文件名</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'filename'</span></code></p></td>
<td><p>SortKey.FILENAME</p></td>
<td><p>文件名</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'module'</span></code></p></td>
<td><p>N/A</p></td>
<td><p>文件名</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'ncalls'</span></code></p></td>
<td><p>N/A</p></td>
<td><p>调用次数</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'pcalls'</span></code></p></td>
<td><p>SortKey.PCALLS</p></td>
<td><p>原始调用计数</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'line'</span></code></p></td>
<td><p>SortKey.LINE</p></td>
<td><p>行号</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'name'</span></code></p></td>
<td><p>SortKey.NAME</p></td>
<td><p>函数名称</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'nfl'</span></code></p></td>
<td><p>SortKey.NFL</p></td>
<td><p>名称/文件/行</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'stdname'</span></code></p></td>
<td><p>SortKey.STDNAME</p></td>
<td><p>标准名称</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">'time'</span></code></p></td>
<td><p>SortKey.TIME</p></td>
<td><p>内部时间</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">'tottime'</span></code></p></td>
<td><p>N/A</p></td>
<td><p>内部时间</p></td>
</tr>
</tbody>
</table>
<p>Note that all sorts on statistics are in descending order (placing most
time consuming items first), where as name, file, and line number searches
are in ascending order (alphabetical). The subtle distinction between
<code class="docutils literal notranslate"><span class="pre">SortKey.NFL</span></code> and <code class="docutils literal notranslate"><span class="pre">SortKey.STDNAME</span></code> is that the standard name is a
sort of the name as printed, which means that the embedded line numbers
get compared in an odd way.  For example, lines 3, 20, and 40 would (if
the file names were the same) appear in the string order 20, 3 and 40.
In contrast, <code class="docutils literal notranslate"><span class="pre">SortKey.NFL</span></code> does a numeric compare of the line numbers.
In fact, <code class="docutils literal notranslate"><span class="pre">sort_stats(SortKey.NFL)</span></code> is the same as
<code class="docutils literal notranslate"><span class="pre">sort_stats(SortKey.NAME,</span> <span class="pre">SortKey.FILENAME,</span> <span class="pre">SortKey.LINE)</span></code>.</p>
<p>For backward-compatibility reasons, the numeric arguments <code class="docutils literal notranslate"><span class="pre">-1</span></code>, <code class="docutils literal notranslate"><span class="pre">0</span></code>,
<code class="docutils literal notranslate"><span class="pre">1</span></code>, and <code class="docutils literal notranslate"><span class="pre">2</span></code> are permitted.  They are interpreted as <code class="docutils literal notranslate"><span class="pre">'stdname'</span></code>,
<code class="docutils literal notranslate"><span class="pre">'calls'</span></code>, <code class="docutils literal notranslate"><span class="pre">'time'</span></code>, and <code class="docutils literal notranslate"><span class="pre">'cumulative'</span></code> respectively.  If this old
style format (numeric) is used, only one sort key (the numeric key) will
be used, and additional arguments will be silently ignored.</p>
<div class="versionadded">
<p><span class="versionmodified added">3.7 新版功能: </span>Added the SortKey enum.</p>
</div>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.reverse_order">
<code class="sig-name descname">reverse_order</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.reverse_order" title="永久链接至目标">¶</a></dt>
<dd><p>This method for the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class reverses the ordering of the
basic list within the object.  Note that by default ascending vs
descending order is properly selected based on the sort key of choice.</p>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.print_stats">
<code class="sig-name descname">print_stats</code><span class="sig-paren">(</span><em class="sig-param">*restrictions</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.print_stats" title="永久链接至目标">¶</a></dt>
<dd><p>This method for the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class prints out a report as described
in the <a class="reference internal" href="#profile.run" title="profile.run"><code class="xref py py-func docutils literal notranslate"><span class="pre">profile.run()</span></code></a> definition.</p>
<p>The order of the printing is based on the last
<a class="reference internal" href="#pstats.Stats.sort_stats" title="pstats.Stats.sort_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">sort_stats()</span></code></a> operation done on the object (subject to
caveats in <a class="reference internal" href="#pstats.Stats.add" title="pstats.Stats.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a> and
<a class="reference internal" href="#pstats.Stats.strip_dirs" title="pstats.Stats.strip_dirs"><code class="xref py py-meth docutils literal notranslate"><span class="pre">strip_dirs()</span></code></a>).</p>
<p>The arguments provided (if any) can be used to limit the list down to the
significant entries.  Initially, the list is taken to be the complete set
of profiled functions.  Each restriction is either an integer (to select a
count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to
select a percentage of lines), or a string that will interpreted as a
regular expression (to pattern match the standard name that is printed).
If several restrictions are provided, then they are applied sequentially.
For example:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">print_stats</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;foo:&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>would first limit the printing to first 10% of list, and then only print
functions that were part of filename <code class="file docutils literal notranslate"><span class="pre">.*foo:</span></code>.  In contrast, the
command:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">print_stats</span><span class="p">(</span><span class="s1">&#39;foo:&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>would limit the list to all functions having file names <code class="file docutils literal notranslate"><span class="pre">.*foo:</span></code>,
and then proceed to only print the first 10% of them.</p>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.print_callers">
<code class="sig-name descname">print_callers</code><span class="sig-paren">(</span><em class="sig-param">*restrictions</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.print_callers" title="永久链接至目标">¶</a></dt>
<dd><p>This method for the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class prints a list of all functions
that called each function in the profiled database.  The ordering is
identical to that provided by <a class="reference internal" href="#pstats.Stats.print_stats" title="pstats.Stats.print_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">print_stats()</span></code></a>, and the
definition of the restricting argument is also identical.  Each caller is
reported on its own line.  The format differs slightly depending on the
profiler that produced the stats:</p>
<ul class="simple">
<li><p>With <a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a>, a number is shown in parentheses after each caller
to show how many times this specific call was made.  For convenience, a
second non-parenthesized number repeats the cumulative time spent in the
function at the right.</p></li>
<li><p>With <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a>, each caller is preceded by three numbers: the
number of times this specific call was made, and the total and
cumulative times spent in the current function while it was invoked by
this specific caller.</p></li>
</ul>
</dd></dl>

<dl class="method">
<dt id="pstats.Stats.print_callees">
<code class="sig-name descname">print_callees</code><span class="sig-paren">(</span><em class="sig-param">*restrictions</em><span class="sig-paren">)</span><a class="headerlink" href="#pstats.Stats.print_callees" title="永久链接至目标">¶</a></dt>
<dd><p>This method for the <a class="reference internal" href="#pstats.Stats" title="pstats.Stats"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code></a> class prints a list of all function
that were called by the indicated function.  Aside from this reversal of
direction of calls (re: called vs was called by), the arguments and
ordering are identical to the <a class="reference internal" href="#pstats.Stats.print_callers" title="pstats.Stats.print_callers"><code class="xref py py-meth docutils literal notranslate"><span class="pre">print_callers()</span></code></a> method.</p>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="what-is-deterministic-profiling">
<span id="deterministic-profiling"></span><h2>什么是确定性性能分析？<a class="headerlink" href="#what-is-deterministic-profiling" title="永久链接至标题">¶</a></h2>
<p><em class="dfn">确定性性能分析</em> 旨在反映这样一个事实：即所有 <em>函数调用</em> 、 <em>函数返回</em> 和 <em>异常</em> 事件都被监控，并且对这些事件之间的间隔（在此期间用户的代码正在执行）进行精确计时。相反，统计分析（不是由该模块完成）随机采样有效指令指针，并推断时间花费在哪里。后一种技术传统上涉及较少的开销（因为代码不需要检测），但只提供了时间花在哪里的相对指示。</p>
<p>In Python, since there is an interpreter active during execution, the presence
of instrumented code is not required to do deterministic profiling.  Python
automatically provides a <em class="dfn">hook</em> (optional callback) for each event.  In
addition, the interpreted nature of Python tends to add so much overhead to
execution, that deterministic profiling tends to only add small processing
overhead in typical applications.  The result is that deterministic profiling is
not that expensive, yet provides extensive run time statistics about the
execution of a Python program.</p>
<p>调用计数统计信息可用于识别代码中的错误（意外计数），并识别可能的内联扩展点（高频调用）。内部时间统计可用于识别应仔细优化的 &quot;热循环&quot; 。累积时间统计可用于识别算法选择上的高级别错误。注意，该分析器中对累积时间的异常处理允许将算法的递归实现与迭代实现的统计信息直接进行比较。</p>
</div>
<div class="section" id="limitations">
<span id="profile-limitations"></span><h2>局限性<a class="headerlink" href="#limitations" title="永久链接至标题">¶</a></h2>
<p>一个限制是关于时间信息的准确性。确定性性能分析存在一个涉及精度的基本问题。最明显的限制是，底层的 &quot;时钟&quot; 只以大约0.001秒的速度（通常）运行。因此，没有什么测量会比底层时钟更精确。如果进行了足够的测量，那么 &quot;误差&quot; 将趋于平均。不幸的是，删除第一个错误会导致第二个错误来源。</p>
<p>第二个问题是，从调度事件到分析器获取时间的调用实际获取时钟状态，这需要 &quot;一段时间&quot; 。类似地，从获取时钟值（然后保存）开始，直到再次执行用户代码为止，退出分析器事件句柄时也存在一定的延迟。因此，多次调用单个函数或调用多个函数通常会累积此错误。以这种方式累积的误差通常小于时钟的精度（小于一个时钟周期），但它 <em>可以</em> 累积并变得非常客观。</p>
<p>与开销较低的 <a class="reference internal" href="#module-cProfile" title="cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code></a> 相比， <a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 的问题更为严重。出于这个原因， <a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 提供了一种针对指定平台的自我校准方法，以便可以在很大程度上（平均地）消除此误差。</p>
</div>
<div class="section" id="calibration">
<span id="profile-calibration"></span><h2>准确估量<a class="headerlink" href="#calibration" title="永久链接至标题">¶</a></h2>
<p><a class="reference internal" href="#module-profile" title="profile: Python source profiler."><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code></a> 模块的 profiler 会从每个事件处理时间中减去一个常量，以补偿调用 time 函数和存储结果的开销。默认情况下，常数为0。对于特定的平台，可用以下程序获得更好修正常数（ <a class="reference internal" href="#profile-limitations"><span class="std std-ref">局限性</span></a> ）。</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">profile</span>
<span class="n">pr</span> <span class="o">=</span> <span class="n">profile</span><span class="o">.</span><span class="n">Profile</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">pr</span><span class="o">.</span><span class="n">calibrate</span><span class="p">(</span><span class="mi">10000</span><span class="p">))</span>
</pre></div>
</div>
<p>The method executes the number of Python calls given by the argument, directly
and again under the profiler, measuring the time for both. It then computes the
hidden overhead per profiler event, and returns that as a float.  For example,
on a 1.8Ghz Intel Core i5 running Mac OS X, and using Python's time.process_time() as
the timer, the magical number is about 4.04e-6.</p>
<p>The object of this exercise is to get a fairly consistent result. If your
computer is <em>very</em> fast, or your timer function has poor resolution, you might
have to pass 100000, or even 1000000, to get consistent results.</p>
<p>当你有一个一致的答案时，有三种方法可以使用：</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">profile</span>

<span class="c1"># 1. Apply computed bias to all Profile instances created hereafter.</span>
<span class="n">profile</span><span class="o">.</span><span class="n">Profile</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">your_computed_bias</span>

<span class="c1"># 2. Apply computed bias to a specific Profile instance.</span>
<span class="n">pr</span> <span class="o">=</span> <span class="n">profile</span><span class="o">.</span><span class="n">Profile</span><span class="p">()</span>
<span class="n">pr</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">your_computed_bias</span>

<span class="c1"># 3. Specify computed bias in instance constructor.</span>
<span class="n">pr</span> <span class="o">=</span> <span class="n">profile</span><span class="o">.</span><span class="n">Profile</span><span class="p">(</span><span class="n">bias</span><span class="o">=</span><span class="n">your_computed_bias</span><span class="p">)</span>
</pre></div>
</div>
<p>If you have a choice, you are better off choosing a smaller constant, and then
your results will &quot;less often&quot; show up as negative in profile statistics.</p>
</div>
<div class="section" id="using-a-custom-timer">
<span id="profile-timers"></span><h2>使用自定义计时器<a class="headerlink" href="#using-a-custom-timer" title="永久链接至标题">¶</a></h2>
<p>If you want to change how current time is determined (for example, to force use
of wall-clock time or elapsed process time), pass the timing function you want
to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Profile</span></code> class constructor:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">pr</span> <span class="o">=</span> <span class="n">profile</span><span class="o">.</span><span class="n">Profile</span><span class="p">(</span><span class="n">your_time_func</span><span class="p">)</span>
</pre></div>
</div>
<p>The resulting profiler will then call <code class="docutils literal notranslate"><span class="pre">your_time_func</span></code>. Depending on whether
you are using <a class="reference internal" href="#profile.Profile" title="profile.Profile"><code class="xref py py-class docutils literal notranslate"><span class="pre">profile.Profile</span></code></a> or <code class="xref py py-class docutils literal notranslate"><span class="pre">cProfile.Profile</span></code>,
<code class="docutils literal notranslate"><span class="pre">your_time_func</span></code>'s return value will be interpreted differently:</p>
<dl>
<dt><a class="reference internal" href="#profile.Profile" title="profile.Profile"><code class="xref py py-class docutils literal notranslate"><span class="pre">profile.Profile</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">your_time_func</span></code> should return a single number, or a list of numbers whose
sum is the current time (like what <a class="reference internal" href="os.html#os.times" title="os.times"><code class="xref py py-func docutils literal notranslate"><span class="pre">os.times()</span></code></a> returns).  If the
function returns a single time number, or the list of returned numbers has
length 2, then you will get an especially fast version of the dispatch
routine.</p>
<p>Be warned that you should calibrate the profiler class for the timer function
that you choose (see <a class="reference internal" href="#profile-calibration"><span class="std std-ref">准确估量</span></a>).  For most machines, a timer
that returns a lone integer value will provide the best results in terms of
low overhead during profiling.  (<a class="reference internal" href="os.html#os.times" title="os.times"><code class="xref py py-func docutils literal notranslate"><span class="pre">os.times()</span></code></a> is <em>pretty</em> bad, as it
returns a tuple of floating point values).  If you want to substitute a
better timer in the cleanest fashion, derive a class and hardwire a
replacement dispatch method that best handles your timer call, along with the
appropriate calibration constant.</p>
</dd>
<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">cProfile.Profile</span></code></dt><dd><p><code class="docutils literal notranslate"><span class="pre">your_time_func</span></code> should return a single number.  If it returns integers,
you can also invoke the class constructor with a second argument specifying
the real duration of one unit of time.  For example, if
<code class="docutils literal notranslate"><span class="pre">your_integer_time_func</span></code> returns times measured in thousands of seconds,
you would construct the <code class="xref py py-class docutils literal notranslate"><span class="pre">Profile</span></code> instance as follows:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">pr</span> <span class="o">=</span> <span class="n">cProfile</span><span class="o">.</span><span class="n">Profile</span><span class="p">(</span><span class="n">your_integer_time_func</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">)</span>
</pre></div>
</div>
<p>As the <code class="xref py py-class docutils literal notranslate"><span class="pre">cProfile.Profile</span></code> class cannot be calibrated, custom timer
functions should be used with care and should be as fast as possible.  For
the best results with a custom timer, it might be necessary to hard-code it
in the C source of the internal <code class="xref py py-mod docutils literal notranslate"><span class="pre">_lsprof</span></code> module.</p>
</dd>
</dl>
<p>Python 3.3 adds several new functions in <a class="reference internal" href="time.html#module-time" title="time: Time access and conversions."><code class="xref py py-mod docutils literal notranslate"><span class="pre">time</span></code></a> that can be used to make
precise measurements of process or wall-clock time. For example, see
<a class="reference internal" href="time.html#time.perf_counter" title="time.perf_counter"><code class="xref py py-func docutils literal notranslate"><span class="pre">time.perf_counter()</span></code></a>.</p>
</div>
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<li><a class="reference internal" href="#">Python 分析器</a><ul>
<li><a class="reference internal" href="#introduction-to-the-profilers">分析器简介</a></li>
<li><a class="reference internal" href="#instant-user-s-manual">即时用户手册</a></li>
<li><a class="reference internal" href="#module-cProfile"><code class="xref py py-mod docutils literal notranslate"><span class="pre">profile</span></code> 和 <code class="xref py py-mod docutils literal notranslate"><span class="pre">cProfile</span></code> 模块参考</a></li>
<li><a class="reference internal" href="#the-stats-class"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stats</span></code> 类</a></li>
<li><a class="reference internal" href="#what-is-deterministic-profiling">什么是确定性性能分析？</a></li>
<li><a class="reference internal" href="#limitations">局限性</a></li>
<li><a class="reference internal" href="#calibration">准确估量</a></li>
<li><a class="reference internal" href="#using-a-custom-timer">使用自定义计时器</a></li>
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